5.2 GHz variable-gain amplifier and power amplifier driver for WLAN IEEE 802.11a transmitter front-end

A 5.2 GHz variable-gain amplifier (VGA) and a power amplifier (PA) driver are designed for WLAN IEEE 802.11a monolithic RFIC. The VGA and the PA driver are implemented in a 50 GHz 0.35 μm SiGe BiCMOS technology and occupy 1.12×1.25 mm~2 die area. The VGA with effective temperature compensation is controlled by 5 bits and has a gain range of 34 dB. The PA driver with tuned loads utilizes a differential input, single-ended output topology, and the tuned loads resonate at 5.2 GHz. The maximum overall gain of the VGA and the PA driver is 29 dB with the output third-order intercept point (OIP3) of 11 dBm. The gain drift over the temperature varying from -30 to 85℃ converges within±3 dB. The total current consumption is 45 mA under a 2.85 V power supply.

Dynamics of nonlinear error growth and season-dependent predictability of El Nino events in the Zebiak-Cane model

With the intermediate-complexity Zebiak-Cane model, we investigate the 'spring predictability barrier' (SPB) problem for El Nino events by tracing the evolution of conditional nonlinear optimal perturbation (CNOP), where CNOP is superimposed on the El Nino events and acts as the initial error with the biggest negative effect on the El Nino prediction. We show that the evolution of CNOP-type errors has obvious seasonal dependence and yields a significant SPB, with the most severe occurring in predictions made before the boreal spring in the growth phase of El Nino. The CNOP-type errors can be classified into two types: one possessing a sea-surface-temperature anomaly pattern with negative anomalies in the equatorial central-western Pacific, positive anomalies in the equatorial eastern Pacific, and a thermocline depth anomaly pattern with positive anomalies along the Equator, and another with patterns almost opposite to those of the former type. In predictions through the spring in the growth phase of El Nino, the initial error with the worst effect on the prediction tends to be the latter type of CNOP error, whereas in predictions through the spring in the decaying phase, the initial error with the biggest negative effect on the prediction is inclined to be the former type of CNOP error. Although the linear singular vector (LSV)-type errors also have patterns similar to the CNOP-type errors, they cover a more localized area than the CNOP-type errors and cause a much smaller prediction error, yielding a less significant SPB. Random errors in the initial conditions are also superimposed on El Nino events to investigate the SPB. We find that, whenever the predictions start, the random errors neither exhibit an obvious season-dependent evolution nor yield a large prediction error, and thus may not be responsible for the SPB phenomenon for El Nino events. These results suggest that the occurrence of the SPB is closely related to particular initial error patterns. The two kinds of CNOP-type error are most likely to cause a significant SPB. They have opposite signs and, consequently, opposite growth behaviours, a result which may demonstrate two dynamical mechanisms of error growth related to SPB: in one case, the errors grow in a manner similar to El Nino; in the other, the errors develop with a tendency opposite to El Nino. The two types of CNOP error may be most likely to provide the information regarding the 'sensitive area' of El Nino-Southern Oscillation (ENSO) predictions. If these types of initial error exist in realistic ENSO predictions and if a target method or a data assimilation approach can filter them, the ENSO forecast skill may be improved. Copyright (C) 2009 Royal Meteorological Society

从赌博游戏中的损失厌恶行为看情感预测与体验在决策中的作用

In Kermer, Driver-Linn, Wilson and Gilbert’s (2006) study on affective forecast, they found that people have a tendency to overestimate affective reactions in gains and losses, and people expect losses to have greater hedonic impact than gains of equal magnitude. Because of thus affective forecasting error, people prefer to irrationally avoid losses. Loss aversion is then seen as both a wealth-maximizing error and an affect-maximizing error. The present study examined the relationships among affective forecast, affective experience and loss aversion, and tested Kermer et al.’s (2006) conclusion that people’s loss aversion is an affective forecasting error. In experiment 1, we examined the relationship between affective forecast and loss aversion. Kermer et al.’s (2006) hypothesized that when people expect losses to have greater hedonic impact than gains, they will accept the gambling task, and when people expect gains to have greater hedonic impact than losses, they will refuse the gambling task. We found that (1) individuals with lower loss aversion had a greater tendency to accept a gambling task than those with higher loss aversion; (2) individuals with lower loss aversion expected losses and gains to have smaller affective impacts than those with higher loss aversion. Thus, people never exactly calculated their forecasting affective. In experiment 2, we examined the relationship between affective forecast and affective experience. Consistent with Kermer et al.’s (2006) finding, we found that our participants tended to overestimate affective reactions in gains as well as losses. More interestingly, Kermer et al.’s (2006) found that participants’ predictions for a loss were significantly more distant from experienced emotions than were their predictions for a win, we, however, found the opposite —participants’ predictions for a win were significantly more distant from the experienced emotions than were their predictions for a loss. These experiments further validated the relations between affection and decision making, and contributed to our understanding on the affective reactions to future events. Our study imply that it was not the exact calculation of affective forecast on decision outcomes, but rather the magnitude of affection on outcomes, that influenced people’s affective decision making. It indicated that those with lower magnitude of affection would less like to avoid losses, and thus more like to accept a gambling task.

Affine Matching with Bounded Sensor Error: A Study of Geometric Hashing and Alignment

Affine transformations are often used in recognition systems, to approximate the effects of perspective projection. The underlying mathematics is for exact feature data, with no positional uncertainty. In practice, heuristics are added to handle uncertainty. We provide a precise analysis of affine point matching, obtaining an expression for the range of affine-invariant values consistent with bounded uncertainty. This analysis reveals that the range of affine-invariant values depends on the actual $x$-$y$-positions of the features, i.e. with uncertainty, affine representations are not invariant with respect to the Cartesian coordinate system. We analyze the effect of this on geometric hashing and alignment recognition methods.

The Alignment of Objects With Smooth Surfaces: Error Analysis of the Curvature Method

The recognition of objects with smooth bounding surfaces from their contour images is considerably more complicated than that of objects with sharp edges, since in the former case the set of object points that generates the silhouette contours changes from one view to another. The "curvature method", developed by Basri and Ullman [1988], provides a method to approximate the appearance of such objects from different viewpoints. In this paper we analyze the curvature method. We apply the method to ellipsoidal objects and compute analytically the error obtained for different rotations of the objects. The error depends on the exact shape of the ellipsoid (namely, the relative lengths of its axes), and it increases a sthe ellipsoid becomes "deep" (elongated in the Z-direction). We show that the errors are usually small, and that, in general, a small number of models is required to predict the appearance of an ellipsoid from all possible views. Finally, we show experimentally that the curvature method applies as well to objects with hyperbolic surface patches.

On the Relationship Between Generalization Error, Hypothesis Complexity, and Sample Complexity for Radial Basis Functions

In this paper, we bound the generalization error of a class of Radial Basis Function networks, for certain well defined function learning tasks, in terms of the number of parameters and number of examples. We show that the total generalization error is partly due to the insufficient representational capacity of the network (because of its finite size) and partly due to insufficient information about the target function (because of finite number of samples). We make several observations about generalization error which are valid irrespective of the approximation scheme. Our result also sheds light on ways to choose an appropriate network architecture for a particular problem.

Error Detection and Recovery for Robot Motion Planning with Uncertainty

Robots must plan and execute tasks in the presence of uncertainty. Uncertainty arises from sensing errors, control errors, and uncertainty in the geometry of the environment. The last, which is called model error, has received little previous attention. We present a framework for computing motion strategies that are guaranteed to succeed in the presence of all three kinds of uncertainty. The motion strategies comprise sensor-based gross motions, compliant motions, and simple pushing motions.

The effect of the product cost factor on error handling in industrial robots

N.W. Hardy and M.H. Lee. The effect of the product cost factor on error handling in industrial robots. In Maria Gini, editor, Detecting and Resolving Errors in Manufacturing Systems. Papers from the 1994 AAAI Spring Symposium Series, pages 59-64, Menlo Park, CA, March 1994. The AAAI Press. Technical Report SS-94-04, ISBN 0-929280-60-1.

Research into error recovery for sensory robots

Lee, M., Barnes, D. P., Hardy, N. (1985). Research into error recovery for sensory robots. Sensor Review, 5 (4), 194-197.

Research into automatic error recovery

Lee, M., Hardy, N., & Barnes, D. P. (1984). Research into automatic error recovery. 65-69. Paper presented at 4th International Conference on Robot Vision and Sensory Controls, London, London, United Kingdom.

Error recovery in robot applications

Lee, M., Hardy, N., & Barnes, D. P. (1983). Error recovery in robot applications. 217-222. Paper presented at 6th British Robot Association Annual Conference, Birmingham, Birmingham, United Kingdom.

Knowledge based error recovery in industrial robots

M. H. Lee, D. P. Barnes, and N. W. Hardy. Knowledge based error recovery in industrial robots. In Proc. 8th. Int. Joint Conf. Artificial Intelligence, pages 824-826, Karlsruhe, FDR., 1983.

Automatic Error Recovery in Behaviour-Based Assistive Robots with Learning from Experience

Meng Q. and Lee M.H., Automatic Error Recovery in Behaviour-Based Assistive Robots with Learning from Experience, in Proc. INES 2001, 5th IEEE Int. Conf. on Intelligent Engineering Systems, Helsinki, Finland, Sept 2001, pp291-296.